If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The problem of planning with partial observability in the presence of a single agent has been addressed as a contingent or POMDP problem. Since the task is computationally hard, on-line approaches have also been developed that just compute the action to do next rather than full policies. In this work, we address a similar problem but in a multiagent setting where agents share a common goal and plan with beliefs which are about the world and the possibly nested beliefs of other agents. For this, we extend the belief tracking formulation of Kominis and Geffner to the on-line setting where plans are supposed to work for the true hidden state as revealed by the observations, and develop an alternative translation into classical planning that is used within a plan-execute-observe-and-replan cycle. Planning is done from the perspective of the agents, and there is a single planning agent in each replanning episode that can change across episodes. We present empirical results and show that interesting agent dialogues arise in this setting where agents collaborate by requesting or volunteering information in a goal-directed manner.
It has been shown recently that heuristic and width-based search can be combined to produce planning algorithms with a performance that goes beyond the state-of-the-art. Such algorithms are based on best-first width search (BFWS), a plain best-first search set with evaluations functions combined lexicographically to break ties, some of which express novelty based preferences. In BFWS(f5), for example, the evaluation function f5 weights nodes by a novelty measure, breaking ties by the number of non-achieved goals. BFWS(f5) is a best-first algorithm, and hence, it is complete but not polynomial, and its performance doesn’t match the state of the art. In this work we show, however, that incomplete versions of BFWS(f5) where nodes with novelty greater than k are pruned, are not only polynomial but have an empirical performance that is better than both BFWS(f5) and state-of-the-art planners. This is shown by considering all the international planning competition instances. This is the first time where polynomial algorithms with meaningful bounds are shown to achieve state-of-the-art performance in planning. Practical and theoretical implications of this empirical finding are briefly sketched.
It has been shown recently that the performance of greedy best-first search (GBFS) for computing plans that are not necessarily optimal can be improved by adding forms of exploration when reaching heuristic plateaus: from random walks to local GBFS searches. In this work, we address this problem but using structural exploration methods resulting from the ideas of width-based search. Width-based methodsseek novel states, are not goal oriented, and their power has been shown recently in the Atari and GVG-AI video-games. We show first that width-based exploration in GBFS is more effective than GBFS with local GBFS search (GBFS-LS), and then proceed to formulate a simple and general computational framework where standard goal-oriented search (exploitation) and width-based search (structural exploration) are combined to yield a search scheme, best-first width search, that is better than both and which results in classical planning algorithms that outperform the state-of-the-art planners.
We consider the problem of deriving formulas that capture traps, invariants, and dead-ends in classical planning through polynomial forms of preprocessing. An invariant is a formula that is true in the initial state and in all reachable states. A trap is a conditional invariant: once a state is reached that makes the trap true, all the states that are reachable from it will sat- isfy the trap formula as well. Finally, dead-ends are formulas that are satisfied in states that make the goal unreachable. We introduce a preprocessing algorithm that computes traps in k- DNF form that is exponential in the k parameter, and show how the algorithm can be used to precompute invariants and dead-ends. We report also preliminary tests that illustrate the effectiveness of the preprocessing algorithm for identifying dead-end states, and compare it with the identification that follows from the use of the h1 and h2 heuristics that cannot be preprocessed, and must be computed at run time.
We establish conditions under which memoryless policies and finite-state controllers that solve one partially observable non-deterministic problem (PONDP) generalize to other problems; namely, problems that have a similar structure and share the same action and observation space. This is relevant to generalized planning where plans that work for many problems are sought, and to transfer learning where knowledge gained in the solution of one problem is to be used on related problems. We use a logical setting where uncertainty is represented by sets of states and the goal is to be achieved with certainty. While this gives us crisp notions of solution policies and generalization, the account also applies to probabilistic PONDs, i.e., Goal POMDPs.
The Atari 2600 games supported in the Arcade Learning Environment (Bellemare et al. 2013) all feature aknown initial (RAM) state and actions that have deterministic effects. Classical planners, however, cannot be used for selecting actions for two reasons: first, nocompact PDDL-model of the games is given, and more importantly, the action effects and goals are not known a priori. Moreover, in these games there is usually no set of goals to be achieved but rewards to be collected. These features do not preclude the use of classical algorithms like breadth-first search or Dijkstra’s algorithm, but these methods are not effective over large state spaces. We thus turn to a different class of classical planning algorithms introduced recently that perform a structured exploration of the state space; namely, like breadth-first search and Dijkstra’s algorithm they are“blind” and hence do not require prior knowledge of state transitions, costs (rewards) or goals, and yet, like heuristic search algorithms, they have been shown to be effective for solving problems over huge state spaces.The simplest such algorithm, called Iterated Width or IW, consists of a sequence of calls IW(1), IW(2), . . . ,IW(k) where IW(i) is a breadth-first search in which a state is pruned when it is not the first state in the search to make true some subset of i atoms. The empirical results over 54 games suggest that the performance of IW with the k parameter fixed to 1, i.e., IW(1), is at the level of the state of the art represented by UCT. A simple best-first variation of IW that combines exploration and exploitation proves to be very competitive as well.
Keyder, Emil, Geffner, Hector
Soft goals extend the classical model of planning with a simple model of preferences. The best plans are then not the ones with least cost but the ones with maximum utility, where the utility of a plan is the sum of the utilities of the soft goals achieved minus the plan cost. Finding plans with high utility appears to involve two linked problems: choosing a subset of soft goals to achieve and finding a low-cost plan to achieve them. New search algorithms and heuristics have been developed for planning with soft goals, and a new track has been introduced in the International Planning Competition (IPC) to test their performance. In this note, we show however that these extensions are not needed: soft goals do not increase the expressive power of the basic model of planning with action costs, as they can easily be compiled away. We apply this compilation to the problems of the net-benefit track of the most recent IPC, and show that optimal and satisficing cost-based planners do better on the compiled problems than optimal and satisficing net-benefit planners on the original problems with explicit soft goals. Furthermore, we show that penalties, or negative preferences expressing conditions to avoid, can also be compiled away using a similar idea.
Palacios, Hector, Geffner, Hector
Conformant planning is the problem of finding a sequence of actions for achieving a goal in the presence of uncertainty in the initial state or action effects. The problem has been approached as a path-finding problem in belief space where good belief representations and heuristics are critical for scaling up. In this work, a different formulation is introduced for conformant problems with deterministic actions where they are automatically converted into classical ones and solved by an off-the-shelf classical planner. The translation maps literals L and sets of assumptions t about the initial situation, into new literals KL/t that represent that L must be true if t is initially true. We lay out a general translation scheme that is sound and establish the conditions under which the translation is also complete. We show that the complexity of the complete translation is exponential in a parameter of the problem called the conformant width, which for most benchmarks is bounded. The planner based on this translation exhibits good performance in comparison with existing planners, and is the basis for T0, the best performing planner in the Conformant Track of the 2006 International Planning Competition.
Belief tracking is a basic problem in planning with sensing. While the problem is intractable, it has been recently shown that for both deterministic and non-deterministic systems expressed in compact form, it can be done in time and space that are exponential in the problem width. The width measures the maximum number of state variables that are all relevant to a given precondition or goal. In this work, we extend this result both theoretically and practically. First, we introduce an alternative decomposition scheme and algorithm with the same time complexity but different completeness guarantees, whose space complexity is much smaller: exponential in the causal width of the problem that measures the number of state variables that are causally relevant to a given precondition, goal, or observable. Second, we introduce a fast, meaningful, and powerful approximation that trades completeness by speed, and is both time and space exponential in the problem causal width. It is then shown empirically that the algorithm combined with simple heuristics yields state-of-the-art real-time performance in domains with high widths but low causal widths such as Minesweeper, Battleship, and Wumpus.
We consider the problem of planning in environments where the state is fully observable, actions have non-deterministic effects, and plans must generate infinite state trajectories for achieving a large class of LTL goals. More formally, we focus on the control synthesis problem under the assumption that the LTL formula to be realized can be mapped into a deterministic Bu ̈chi automaton. We show that by assuming that action non-determinism is fair, namely that infinite executions of a non-deterministic action in the same state yield each possible successor state an infinite number of times, the (fair) synthesis problem can be reduced to a standard strong cyclic planning task over reachability goals. Since strong cyclic planners are built on top of efficient classical planners, the transformation reduces the non-deterministic, fully observable, temporally extended planning task into the solution of classical planning problems. A number of experiments are reported showing the potential benefits of this approach to synthesis in comparison with state-of-the-art symbolic methods.