	VIVOMIND PROPRIETARY CATALOG OF AGENTS - 2008

	ALGORITHM SERVER AGENTS

	1. 
	IntentAgent.pl
	Loads plan structures – has both dynamic plan recognition and plan synthesis capabilities – loads a user specified file of plan fragments and an ontology

	2. 
	LinkAnalysisAgent.pl
	Has a model and extensible grammar for link creation and analysis

	3. 
	AbductiveCostBasedAgent.pl
	An implementation of Mark Stickels Abductive Reasoning Engine with costs for abductions

	4. 
	MarkovLogicAgent.pl
	Prolog Markov Logic Model – creates prolog markov logic trees – can be extended to arbitrary networks, VLMM’s etc…

	5. 
	AnalogyEquationServices.pl
	Uses prolog clause generalization for equational reasoning by analogies

	AGENT INTERFACE SERVICES

	6. 
	displayAdaptor.pro
	GUI Display Window

	7. 
	HTML_Adaptor.pro
	HTML Display 

	8. 
	HTTPMicroServer.pro
	Serves Web Based Clients

	9. 
	FMF
	FMF Framework group of visual elements

	10. 
	NetworkLayerAdapter.pro
	TCP/IP Network Layer Adaptor

	LOW LEVEL FILTER AGENTS

	11. 
	agtfilter_behaviorfilter.pro
	Rate of communications, message, time per actor

	12. 
	agtfilter_morphologyfilter.pro
	Morphology (English lexical)

	13. 
	agtfilter_phrazefilter.pro
	Common phrases (template based) ex. IP URL’s

	14. 
	agtfilter_setofwordsfilter.pro
	Bag of Words representation with selectable filters – example, cut out words less than 3 chars

	15. 
	agtfilter_statisticsfilter.pro
	Message length and Count of the p/d/w/s (punctuation/digits/words/special) chars found

	16. 
	agtfilter_stoplist.pro
	Common stop-words or stop conditions

	17. 
	agtfilter_subsequence.pro
	Dynamic programming of least common subsequences, most common subsequences

	18. 
	agttransducer_tokens.pro
	Normalizes text into standard filtered form (anotated with meta-data) – example, all upper case is lowered, 

	19. 
	agttranslator_tokens.pro
	Translator of inputs removing non-recognized characters (filter out unknowns)

	20. 
	ide_dataprefilterservice.pro
	Prefilter message buffers – reacts to message sources, like files, dbms’s etc...

	21. 
	ide_metadatafilter.pro
	MetaData filter – instruments filter agents

	22. 
	ide_monitor.pro
	Monitor to permit user to view into any agent

	23. 
	ide_tokenMetaDatafilter.pro
	Token MetaData Filter  - annotates text, dispatches to blackboards and creates textDB data storage files on disk

	INFORMATION FILTERS

	24. 
	agt_BasicInformationFilter.pro
	Technical Indicators:  Moving averages, standard deviations, least squares, and other measures 

	25. 
	ide_behaviorModel.pro
	Reaction-rates of actors, hypothesize humans versus bots

	26. 
	ide_markovModel.pro
	Markov Algorithms and Models – markov-tree representation and Markov Logic – calls several other lower level agents

	27. 
	mkvagt_inputtext.pro
	Markov text model 

	28. 
	mkvagt_markov.pro
	Markov Sematon agent 

	29. 
	mkvagt_markovModeler.pro
	Markov model generation for actors, content, or other properties analyzable

	30. 
	mkvagt_mtokenizer.pro
	Markov tokenizer (selects ”what” to markov model)

	31. 
	ide_symbolicfilterModel.pro
	Symbolic dynamics filter (ex. Strip out least frequent or most frequent or periodically appearing tokens)

	32. 
	ide_vectorModel.pro
	Information Vector based processor, traditional and Geometric – uses scalar products, geometric products, inner and outer products. Augments traditional IR models and adds onto them

	33. 
	txagt_vectorspaces.pro
	Traditional vector, inverse index algorithms

	34. 
	txagt_geometricspaces.pro
	Geometric Spaces – Clifford Algebra Models using Bigram or Distance based word to word Bivector representations

	35. 
	txagt_agglomcluster.pro
	Agglomerative Clustering

	36. 
	txagt_beliefnet.pro
	Belief Network (simple and naive algorithm)

	37. 
	txagt_bnhypothesis.pro
	Hypothesizer based on belief net construction

	38. 
	txagt_discretenaivebayes.pro
	Discrete Naive Bayes algorithm

	39. 
	txagt_files.pro
	Vector file manager

	40. 
	txagt_inductiondecisiontree.pro
	Decisition Tree induction algorithm

	41. 
	txagt_knearestneighbors.pro
	Knn-Nearest Neighbors

	42. 
	txagt_predutils.pro
	Support agent to routines and utilities – was originally going to be a Mathematica-Server based API (using Mathematica Models as a large and ready source of “agents”)

	43. 
	txagt_txtagentapi.pro
	The main API for accessibility to the models for text message vector processes

	KNOWLEDGE EXTRACTORS

	44. 
	hyp_by_common_phrases.pro
	If two actors use common phrases (induced at runtime) then assume they are in dialog

	45. 
	hyp_dialoging_actors.pro
	If actors are using and introducing new terms, but share vocabulary, assume in dialog

	46. 
	hyp_lc_entities.pro
	If actors are introducing or using least common entities, assume these are references and topic identifiers

	47. 
	ide_corefhypothesizer.pro
	If actors name each other, or name do not acknowledge other parties in the dialog, assume a two-way focussed dialog

	48. 
	ide_textinformationfilters.pro
	Sub-agents (threads) for computing the profile of language – if actors language profiles match, disregard change in alias names and assume they belong to same person or *group*.  Define content by profiles as a “signature” to identify content *purpose* learned by a training cycle: INFORMATION FILTER AGENT:

  (1) average word length, standard deviation, skewness, and kurtosis;

  (2) average sentence length, standard deviation, skewness, and kurtosis;

  (3) token count (total numbers of words);

  (4) type count( number of different words);

  (5) diversity index;

  (6) vocabulary richness;

  (7) vocabulary_hidden_index;

  (8) word frequencies;

  (9) first person pronoun index;

  (10) second person pronoun index;

  (11) text diversity measure (using various bitvector measures – tanimoto, jaccard, Koehler)

  (12) readability index.

  (13) Chaos Game Representation (CGR) of Words using extracted alphabet (TBD)

  (14) Information Temperature of a text (TBD)

  (15) Fractal Map (CGR of sentence)  (TBD)

	CORE REASONING

	49. 
	agt_queries.pro
	Knowledge layer query manager to lower level agents and to this layer

	50. 
	ide_csliverbsemapiservice.pro
	CLSI Verb Ontology – has entailment relations drawn from public sources and thematic structures

	51. 
	ide_geolocateagent.pro
	Geolocation using public API to internet backbone map to geolocate actors based on the ip-addresses captured via exchanges

	52. 
	ide_lexiconapiservice.pro
	Lexicon API (to wordnet and sub agents)

	53. 
	ide_metasearchagent.pro
	GOOGLE Api to internet search (to use the WWW as an ONLINE realtime DB)

	54. 
	ide_rogetapiservice.pro
	Roget’s Thesaurus API (public domain version)

	55. 
	ide_rstagent.pro
	Rhetorical Structure Agent (simple version)

	56. 
	ide_semanticdistance.pro
	Semantic Distance Agent (can use lexical as well as non-lexical, eg. Percept, sources for computations)

	57. 
	ide_vaeapi.pro
	VivoMind Analogy Engine API (used for analogy finding between structure mapping processes)

	GAME-THEORETIC AGENTS

	58. 
	ide_agt_BPSocialchoice.pro
	Bandit Process –statistical decision model of an agent trying to optimize decisions while improving information at the same time. The indices of the arms can be computed separately and then put together to generate an optimal strategy (Gittins-Jones Theorem) – used for characterization of optimal plans. Currently used with other agents as means to see the dynamic allocation index used for hypothesis selection (ie. hypotheses selection is like playing bandit – you can hit the jackpot).

	59. 
	ide_agt_beliefpotentialbid.pro
	Monderer and Samet (1989) introduced a natural language for characterizingplayers’ higher order beliefs. Fix a probability p ( (0, 1]. Let Ω be a set of possible states and let E be any subset of Ω. The event E is p-believed at state ω among

some fixed group of individuals if everyone believes that it is true with probability

at least p.  Used for representing common knowledge using common belief (since actual knowledge is not directly perceived, onlly the win/loose or reward/punish from histories of behaviors)

	60. 
	ide_agt_bimatrixgaming.pro
	Between pairs of agents, compute the best response (i.e. choosing a strategy)

	61. 
	ide_agt_coopesocialchoices.pro
	Cooperative Bidding on social choices – coalition formation (societies) and bidding on hypotheses.  This agent tries to define a nucleolus of the game in order to capture the notion of an optimal society.

	62. 
	ide_agt_hypcostbasedbidder.pro
	Distributed Planning of allocating agents to tasks based on costs to complete: 

fcost (t) fcost (createAgent(t)) fcost (executeJob(t))



	63. 
	ide_agt_perfectinfogaming1.pro
	Extensive Form Games (Game Trees) with rational decision making base on utility, preference and the game model – used to determine membership in societies or autonomous voting – an agent that looses all the time will leave.

	64. 
	ide_agt_perfectinfogaming2.pro
	Bonanno-Vilks propositional logic modeling for game tree.  Aumann theorem of the backward induction under common knowledge model.

	65. 
	ide_agt_perfectinfogaming3.pro
	Belief based strong/weak strategies

	66. 
	ide_agt_signallingbidding.pro
	Signal Game based Bidding using Dempster-Shafer or Bayes evidential measures as observable signals messages and the receiver agents that select best actions to pursue get rewarded.

	67. 
	ide_agt_socialchoicepref.pro
	Societal social choice preferences utilities – treats the pool of hypotheses as a market and makes choices based on preferences. Contains cellular society models.

	68. 
	ide_agt_votingtraders.pro
	Hypotheses are traded on a hypothesis market by sophist, rational,cassandra,naïve agents – the cassandra agents try to predict the future based on their own histories.

	PORTFOLIO THEORY AGENTS

	69. 
	agt_tverskysocialchoice.pro
	Elimination by Aspects model with preferences. Each alternative is viewed as a set of aspects. At each stage an aspect is selected. All alternatives that do not include that aspect are eliminated. Process stops when all but one alternative are eliminated.  Used in making choices for hypotheses and outcomes.

	70. 
	ide_agt_DSbeliefbidding.pro
	Dempster Shafer – bidding based on Dempster-Shafer based beliefs (evidential beliefs). For situations in which information is ambiguous and only part of it can be probabilized. Information can be modeled through DS-belief functions.



	71. 
	ide_agt_frontieranalysis.pro
	Frontier Analysis - Data Envelopment Analysis - aka Frontier Analysis Data Envelopment Analysis (DEA) is a clustering methodology for 

records in data sets corresponding to entities sharing a common list of attributes. Broadly defined, DEA partitions the records into

two subsets; those 'efficient' and those 'inefficient.' An efficient record is one which lies on a specific portion of the boundary of a finitely generated polyhedral set in the dimension of the 

attribute space known as the 'frontier'; inefficient points are those located elsewhere. In traditional applications, DEA frontiers are nonparametric surrogates for unknown theoretical efficiency limits. More generally, however, frontiers are subsets of the boundary defined by extreme elements of the data set. DEA deals with problems that require modeling both qualitative and quantitative data.:

DEA deals with: 

(1) imprecise data, 

(2) inaccurate data, 

(3) missing data, 

(4) qualitative data, 

(5) outliers, 

(6) undesirable outputs, 

(7) quality data, 

(8) statistical analysis

	72. 
	ide_agt_hypcost_am.pro
	Abreu-Matsushima market mechanism for layer 5 sub-societies.  Agents formed into model groups with managers and workers, payoffs estimate values of hypotheses to goals and bid based on goala at hand.

	73. 
	ide_agt_laghypscheduling.pro
	Lagrange hyp prod scheduling – scheduling agents based on costs, efficiency, capabilities and deadlines for delivery of results

	74. 
	ide_agt_managehierarchsco.pro
	Managerial Hierarchy Social Choices – creates hierarchical societies and manages them for performing analytic work based on cost of production and value created.

	75. 
	ide_agt_networkbasedagents.pro
	Individual agents and price setting societies model with coordinators – network organizational structure.

	76. 
	ide_agt_RSDSdb.pro
	Rouch Set Dempster-Shafer Agent can reason and make implications based on evidence using evidence-functions to reduce combinatorial complexity of Dempster-Shafer methods.

	77. 
	ide_agt_thinkingstrategically.pro
	Thinking Strategically using Preference Rules

	78. 
	tverskyCMAgent.pl
	A contrast model agent

	DATA SOURCES / SENSORS

	79. 
	AbstractDataSource.pro
	Buffer for the data from any source

	80. 
	syntheticRawDataHandler.pro
	Buffer for the data from the synthetic source

	ADVANCED REASONING AND FINANCIAL RISK MODELING

	81. 
	Cognitive Memory (a.k.a VAE-2)
	Ultra High Speed Graph / Analogy Agent

	82. 
	fuzzyTreeUnification.pro
	Fuzzy unification for Prolog Engine

	83. 
	RippleDownRules.pro
	Ripple Down Rules Engine

	84. 
	AbductiveModelAgent.pro
	Uses rule generating abduction to test for unforeseen or non-obvious links in data 

	85. 
	FuzzyRules.pro
	Fuzzy Rules Engine

	86. 
	AbductiveSemanticTableaux.pro
	Abductive Semantic conjecture making system

	87. 
	QualitativeReasoningAgent.pro
	Uses qualitative variables for system reasoning

	88. 
	DCTGGLogic.pro
	Definite Clause Transformation Grammar based Genetic Language Programming Agent

	89. 
	PeirceAbductiveAnalogyAgent.pro
	Uses analogies to form constrained abductions

	90. 
	HTNAgent.pro
	Hierarchical Task Net Agent Planner

	91. 
	vickrey-clarke-groves_agent.pl
	Gold standard in the field of “mechanism design”: public goods economy, dynamic behavior : agent beliefs are functions of previous strategy profiles.

	92. 
	seller_agent2.pl
	Basic routines and mechanisms the functionalities of the Seller Agents (given rules and advice)

	93. 
	secretary_agent.pl
	Secretaries Algorithm for “relative rank” and stopping problem : this manages  agents

	94. 
	Scarfs-shoven-whalley_agent.pl
	Consumer producer : Shoven Whalley Economic agents – decompose outcomes into multi-part causes 

	95. 
	rulestrading_multiagents.pl
	Incorporating rules and heuristics – uses an Agent Ontology: 1. ex_ante 2. naïve 3. sophist 4. rational

	96. 
	plausibilitydecision_agent.pl
	Decision making in the presence of ambiguities --- using modal plausible arguments

	97. 
	pandora_agent.pl
	Optimal “order of information” agent – used with genetic algorithms agents and secretaries

	98. 
	networkpricing_multiagents.pl
	Use of networke mechanisms and heirarchies for pricing and decision structures

	99. 
	nash_multiagents2.pl
	Nash implementation theory – preference models

	100. 
	nash_multiagent.pl
	Generate and test on various models of Nash

	101. 
	nashinformationgame_agent.pl
	Perfect information gaming

	102. 
	nashequilibriumgame_agents.pl
	Nash, mixed, and undominated strategy equilibrium

	103. 
	nashchoice_multiagent.pl
	Nash implementation theory (social choice equilibrium theory)

	104. 
	informationgame_model.pl
	Rational choice information game agent

	105. 
	id3-learning_agent.pl
	ID3 quinlan

	106. 
	geneticlearner_agent.pl
	Grammar Evolution Genetic Algorithm --- the grammar is a controlled language of causes, effects

	107. 
	geneticlearner_agent.pl
	Holland Genetic Algorithm – bitmap evolver

	108. 
	galoisdesigner_agent.pl
	Galois Field designer agent – synthesis of structures

	109. 
	galeshapey_marriageagent.pl
	The marriage preferences social model for inter-agent cooperation, collaboration and coordination

	110. 
	fixpointed_agent.pl
	Negotiator Agent that using fixpoints for CONTRACT maps (ie contracts with payoffs)

	111. 
	evidencebelief_agent.pl
	Belief evidence and SIGNALS interpretation uncertainity

	112. 
	epistemicbelief_multiagents.pl
	Epistemic conditions for Nash Equilibrium

	113. 
	epistemicbelief_agent.pl
	Logic of Belief Change and Belief dynamics by unawareness and partition of information

	114. 
	dempstershafer_agent.pl
	Dempster-Shafer evidence model agent

	115. 
	decisiontree_agent.pl
	Decision Tree Agent – makes top decision tree analysis and learns rules

	116. 
	clarkgroves_agent.pl
	Incentives and pricing in teams – auction oriented agents

	117. 
	choquet_agent.pl
	The amount and quality of the information a decision maker (DM) has about the relevant events
affects her preferences. In particular, DMs prefer to act on events they feel
well-informed about, an attitude which has been called ambiguity aversion.

	118. 
	buyer_agent.pl
	Buying Agent by space partitions

	119. 
	bayesperfectequlibrium_agent.pl
	Bayes Equilbrium, decisions

	120. 
	backwrdsinduction_agent.pl
	Backwards Induction agent – game logical agent : Think ; Decide ; Act

	121. 
	vickrey-clarke-groves_agent.pl
	Gold standard in the field of “mechanism design”: public goods economy, dynamic behavior : agent beliefs are functions of previous strategy profiles.

	122. 
	seller_agent2.pl
	Basic routines and mechanisms the functionalities of the Seller Agents (given rules and advice)

	123. 
	secretary_agent.pl
	Secretaries Algorithm for “relative rank” and stopping problem : this manages  agents

	124. 
	Scarfs-shoven-whalley_agent.pl
	Consumer producer : Shoven Whalley Economic agents – decompose outcomes into multi-part causes 

	125. 
	rulestrading_multiagents.pl
	Incorporating rules and heuristics – uses an Agent Ontology: 1. ex_ante 2. naïve 3. sophist 4. rational

	126. 
	plausibilitydecision_agent.pl
	Decision making in the presence of ambiguities --- using modal plausible arguments

	127. 
	pandora_agent.pl
	Optimal “order of information” agent – used with genetic algorithms agents and secretaries

	128. 
	networkpricing_multiagents.pl
	Use of networke mechanisms and heirarchies for pricing and decision structures

	129. 
	nash_multiagents2.pl
	Nash implementation theory – preference models

	130. 
	nash_multiagent.pl
	Generate and test on various models of Nash

	131. 
	nashinformationgame_agent.pl
	Perfect information gaming

	132. 
	nashequilibriumgame_agents.pl
	Nash, mixed, and undominated strategy equilibrium

	133. 
	nashchoice_multiagent.pl
	Nash implementation theory (social choice equilibrium theory)

	134. 
	informationgame_model.pl
	Rational choice information game agent

	135. 
	Chaos_model.pl
	Chaos game dynamic agent

	136. 
	Mathematica_Agent.pl
	Interfaces and uses Mathematica Notebooks

	137. 
	Case_Based_Reasoning.pl
	Case based and analogical reasoner

	138. 
	Chaos_model.pl
	Uses chaos game representations

	139. 
	ProcessPlanner_model.pl
	Planner for Process based workflows, 

	140. 
	Semantic Distance Field Agent
	Similarity by semantic distances

	141. 
	Modal Logic_Agent.pl
	Different kinds of modal logic theories

	142. 
	PatternSequencingAgent.pl
	Linear pattern sequences & subsequences

	143. 
	FuzzyLogicAgent.pl
	Matches predicate arguments approximately

	144. 
	Fuzzy Predicate Agent.pl
	Matches predicate functors approximately

	145. 
	KozaGeneticModelAgent.pl
	Evolution using ADF trees of Koza’s theory

	146. 
	Evolvolog.pl
	Functional forth-like schema agent

	147. 
	infonParser.pl
	Keith Devlin’s INFON theory based parser

	148. 
	CLCE.pl
	Common Logic Controlled English

	149. 
	Intellitext Parser.pl
	Proto-Ontology extractor called “intellitext”

	150. 
	Sematon Agent.pl
	Dynamical Conceptual Graph Process Agent

	151. 
	Linda Agent Society Model.pl
	Protocols and Spaces for Agent Societies

	152. 
	SCIFF Agent.pl
	SCIFF Abduction algorithm agent

	153. 
	GraphEvolutionAgent.pl
	Graph structure based evolution

	154. 
	GraphDescriptorAgent.pl
	Computes graph descriptors

	155. 
	GraphTopoCompressionAgent.pl
	Compresses graph topologies to numbers

	156. 
	FastIndependentComponents.pl
	Independent Components Analysis

	157. 
	SDFM_Agent.pl
	Semantic Distance Field Model (SDFM) using Morse-Smale complexes and Reeb-Graphs

	158. 
	RelationLearningAgent.pl
	Logic based relational learner agent

	159. 
	HolographicMemoryAgent.pl
	Tony Plate’s Holographic HRR model Agent

	160. 
	CounterfactualEmotionAgent.pl
	Simulative states for emotional ascriptions


